A Production Scheduling Problem Using Genetic Algorithm Presented by: Ken Johnson R. Knosala, T. Wal Silesian Technical University, Konarskiego Gliwice, Poland

Introduction The way of Flexible Manufacturing cell work Scheduling with the aid of genetic algorithm and draft of code strings, Results obtained by computer program have been presented. In the first case it has been assumed that the cell works in optional mode (every operation can be done on every machine) In the second, each works in sequential mode (the first operation is executed on the first machine, the second operation on the second, etc…) The only criterion of evaluation is the time of work. (shortest for a finite number of jobs and machines).

Genetic Algorithms Search algorithms, based on natural selection mechanisms and heredity. They join the survival principle of the best fitted strings with systematic information exchange. In every generation the new group of artificial organisms, made from the fusion of the best fitted representatives fragments of previous generation, come into existence.

Genetics

Task Parameters (values of function domain) must be transformed to the code strings. 1. they do not directly transform task parameters, but their coded form. 2. they lead searching, coming out not from one point, but from some population of points. 3. they use only fitness function, but do not use derivative or other auxiliary information.

Design Principles First block defines which jobs are first taken into consideration Within each job are the operations in order of succession when machining

Program Structure Program leads operations of genetic algorithm for 600 generations (it is constant, assumed number). There are 30 individuals (code strings) in every generation.

Fitness Function Maximizes work time of longest working machine Singles out the worst, and gets rid of it Takes bottle-necking into account

Crossover

Mutation Ensures ‘natural selection’ is following the best route Occurs in both 1 st and 2 nd blocks In 2 nd block, a ‘double’ mutation occurs

Models Scheduling 3 jobs to 2 machines:

Results In the form of Gantt Charts For a more complex problem:

Results Reached “ near optimal ” solution very fast (by 200 generations)

Conclusions Genetic algorithm has generated correct schedules Not sure that the solution is optimal. Number of jobs and their operations have not had influence on quality of obtained results Gained schedules have been correct for all cases, that means strings assure right Applied structure of code string has assured good, but not the best, efficiency of creation and propagation of schemes Assured high adjustment of strings